Abstract
BackgroundIn diploid cells, it is important to construct maternal and paternal Hi-C contact maps respectively since the two homologous chromosomes can differ in chromatin three-dimensional (3D) organization. Though previous softwares could construct diploid (maternal and paternal) Hi-C contact maps by using phased genetic variants, they all neglected the systematic biases in diploid Hi-C contact maps caused by variable genetic variant density in the genome. In addition, few of softwares provided quantitative analyses on allele-specific chromatin 3D organization, including compartment, topological domain and chromatin loop.ResultsIn this work, we revealed the feature of allele-assignment bias caused by the variable genetic variant density, and then proposed a novel strategy to correct the systematic biases in diploid Hi-C contact maps. Based on the bias correction, we developed an integrated tool, called HiCHap, to perform read mapping, contact map construction, whole-genome identification of compartments, topological domains and chromatin loops, and allele-specific testing for diploid Hi-C data. Our results show that the correction on allele-assignment bias in HiCHap does significantly improve the quality of diploid Hi-C contact maps, which subsequently facilitates the whole-genome identification of diploid chromatin 3D organization, including compartments, topological domains and chromatin loops. Finally, HiCHap also supports the data analysis for haploid Hi-C maps without distinguishing two homologous chromosomes.ConclusionsWe provided an integrated package HiCHap to perform the data processing, bias correction and structural analysis for diploid Hi-C data. The source code and tutorial of software HiCHap are freely available at https://pypi.org/project/HiCHap/.
Highlights
In diploid cells, it is important to construct maternal and paternal Hi-C contact maps respectively since the two homologous chromosomes can differ in chromatin three-dimensional (3D) organization
Variable Single nucleotide polymorphism (SNP) density leading to systematic biases in diploid Hi-C data Previous study has shown that several steps of Hi-C experiment can lead to systematic biases in Hi-C data [29, 30]
Our results show that the additional correction on the allele-assignment bias caused by variable SNP density can significantly improve the quality of constructed maternal and paternal contact matrices at various resolutions, which indicates that the previous correction methods in haploid contact maps cannot completely eliminate this specific type of bias in diploid Hi-C data
Summary
Variable SNP density leading to systematic biases in diploid Hi-C data Previous study has shown that several steps of Hi-C experiment can lead to systematic biases in Hi-C data [29, 30]. We identified compartments and topological boundaries in all three data sets and chromatin loops in cell line GM12878 from haploid contact maps, HiCHap contact maps and SNP-biased contact maps, and the results show the same trends as correlation analyses (Supplementary figure 8). Our results show that the SNP-bias correction in HiCHap can significantly improve the quality of reconstructed contact matrices at various resolutions in different cell types, which further facilitates the diploid identification of compartment, topological domain and chromatin loop. It should be noted that the benefit of SNP-bias correction is impacted by the number of phased SNPs. If the SNPs are highly dense for given resolutions, such as the presented cases in cell type ICM, the diploid Hi-C contact maps derived from VC normalization alone show relatively similar results to those derived from the allele-assignment bias correction plus VC normalization.
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